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From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ...
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Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge ...
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Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation ...
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Statistical modality tagging from rule-based annotations and crowdsourcing ...
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Use of Modality and Negation in Semantically-Informed Syntactic MT ...
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Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ...
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Computing Lexical Contrast ...
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Abstract:
Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually-created lexicons focus on opposites, such as {\rm hot} and {\rm cold}. Opposites are of many kinds such as antipodals, complementaries, and gradable. However, existing lexicons often do not classify opposites into the different kinds. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as {\rm warm} and {\rm cold} or {\rm tropical} and {\rm freezing}. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, $A$ and $B$, are contrasting, then there is a pair of opposites, $C$ and $D$, such that $A$ and $C$ are strongly related and $B$ and $D$ are strongly related. (For example, there exists the pair of opposites {\rm hot} and {\rm cold} such that {\rm tropical} is related to {\rm ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1308.6300 https://dx.doi.org/10.48550/arxiv.1308.6300
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